Ascend.io

AUGUST 15, 2023

This same principle holds true in data management. It’s no surprise, then, that the quest for Fivetran alternatives is on the rise as organizations set their sights on a more holistic data approach. Defense: Saving Money with Intelligent Data Refresh In football, a solid defense does more than just stop goals.

Data Management 52

Data Management Management Data Ingestion Data Pipeline 52

ProjectPro

FEBRUARY 16, 2023

With so much riding on the efficiency of ETL processes for data engineering teams, it is essential to take a deep dive into the complex world of ETL on AWS to take your data management to the next level. Data integration with ETL has changed in the last three decades.

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AWS Data Management ETL Tools Management 52

Ascend.io

APRIL 12, 2023

So when we talk about making data usable, we’re having a conversation about data integrity. Data integrity is the overall readiness to make confident business decisions with trustworthy data, repeatedly and consistently. Data integrity is vital to every company’s survival and growth.

Data Pipeline 52

Data Pipeline Data Integration Datasets Data 52

AltexSoft

SEPTEMBER 10, 2021

To get a single unified view of all information, companies opt for data integration. In this article, you will learn what data integration is in general, key approaches and strategies to integrate siloed data, tools to consider, and more. What is data integration and why is it important?

Data Integration 52

Data Integration Data Consolidation Data Warehouse BI 52

Ascend.io

OCTOBER 28, 2024

What is Data Transformation? Data transformation is the process of converting raw data into a usable format to generate insights. It involves cleaning, normalizing, validating, and enriching data, ensuring that it is consistent and ready for analysis. This is crucial for maintaining data integrity and quality.

Raw Data 52

Raw Data Datasets Aggregated Data Data Pipeline 52

Towards Data Science

FEBRUARY 6, 2024

As you do not want to start your development with uncertainty, you decide to go for the operational raw data directly. Accessing Operational Data I used to connect to views in transactional databases or APIs offered by operational systems to request the raw data. Does it sound familiar?

Systems 78

Systems Raw Data Metadata Data Cleanse 78

Ascend.io

NOVEMBER 21, 2023

The emergence of cloud data warehouses, offering scalable and cost-effective data storage and processing capabilities, initiated a pivotal shift in data management methodologies. Extract The initial stage of the ELT process is the extraction of data from various source systems. What Is ELT? So, what exactly is ELT?

Raw Data 52

Raw Data Data Warehouse Data Cleanse Data Integration 52

ThoughtSpot

MARCH 5, 2024

While AI-powered, self-service BI platforms like ThoughtSpot can fully operationalize insights at scale by delivering visual data exploration and discovery, it still requires robust underlying data management. Snowflake's new dynamic tables feature redefines how BI and analytics teams approach data transformation pipelines.

BI 111

BI Datasets Raw Data SQL 111

The Modern Data Company

FEBRUARY 2, 2024

Ever wondered why building data-driven applications feels like an uphill battle? It’s not just you – turning raw data into something meaningful can be a real challenge. This approach doesn’t just solve existing problems; it paves the way for a new era of efficiency and effectiveness in data management.

Raw Data 75

Raw Data Utilities Government Data 75

Precisely

SEPTEMBER 19, 2024

Key Takeaways: Data integrity is essential for AI success and reliability – helping you prevent harmful biases and inaccuracies in AI models. Robust data governance for AI ensures data privacy, compliance, and ethical AI use. Proactive data quality measures are critical, especially in AI applications.

Data Governance 78

Data Governance Government High Quality Data Datasets 78

Towards Data Science

NOVEMBER 16, 2023

Data Management A tutorial on how to use VDK to perform batch data processing Photo by Mika Baumeister on Unsplash Versatile Data Ki t (VDK) is an open-source data ingestion and processing framework designed to simplify data management complexities. link] Summary Congratulations!

Data Process 74

Data Process Process Raw Data Data 74

Edureka

AUGUST 2, 2023

Organisations and businesses are flooded with enormous amounts of data in the digital era. Raw data, however, is frequently disorganised, unstructured, and challenging to work with directly. Data processing analysts can be useful in this situation. What does a Data Processing Analysts do ?

Data Process 52

Data Process Process Data Cleanse Data Mining 52

Ascend.io

JULY 11, 2024

It enhances data quality, governance, and automation, transforming raw data into valuable insights. This is what managing data without metadata feels like. For example, timestamps verify data freshness and reliability. It also enables data profiling, analyzing data to understand its structure and quality.

Metadata 52

Metadata IT Government High Quality Data 52

Databand.ai

AUGUST 30, 2023

Data testing tools are software applications designed to assist data engineers and other professionals in validating, analyzing, and maintaining data quality. There are several types of data testing tools. This is part of a series of articles about data quality.

Data Cleanse 52

Data Cleanse Data Validation Data Pipeline Datasets 52

Ascend.io

SEPTEMBER 16, 2024

Managing complex processes is not without its challenges. And yet, advances in automation and cloud computing offer new ways to streamline and enhance data flows, paving the way for more effective data management. In this post, we’ll explore how to design, build, and scale your data flows.

Designing 52

Designing Cloud Computing Data Pipeline Raw Data 52

Knowledge Hut

JANUARY 30, 2024

In today's world, where data rules the roost, data extraction is the key to unlocking its hidden treasures. As someone deeply immersed in the world of data science, I know that raw data is the lifeblood of innovation, decision-making, and business progress. What is data extraction?

ETL Tools 52

ETL Tools Database-centric Data Mining Raw Data 52

The Modern Data Company

APRIL 15, 2024

Transforming Data Complexity into Strategic Insight At first glance, the process of transforming raw data into actionable insights can seem daunting. The journey from data collection to insight generation often feels like operating a complex machine shrouded in mystery and uncertainty.

Building 52

Building Raw Data Food Data 52

Knowledge Hut

APRIL 25, 2024

Data management The process of obtaining, storing, and using data in a cost-effective, effective, and secure way is known as data management. As businesses increasingly rely on intangible assets to create value, an efficient data management strategy is more important than ever.

Big Data 52

Big Data Data Analytics Manufacturing Retail 52

Databand.ai

AUGUST 30, 2023

Unified DataOps represents a fresh approach to managing and synchronizing data operations across several domains, including data engineering, data science, DevOps, and analytics. Organizations need to automate various aspects of their data operations, including data integration, data quality, and data analytics.

Data Governance 40

Data Governance Data Cleanse Government Data Science 40

Databand.ai

AUGUST 30, 2023

DataOps is a collaborative approach to data management that combines the agility of DevOps with the power of data analytics. It aims to streamline data ingestion, processing, and analytics by automating and integrating various data workflows.

Architecture 52

Architecture Data Ingestion Data Governance Data Cleanse 52

DataKitchen

SEPTEMBER 21, 2023

L1 is usually the raw, unprocessed data ingested directly from various sources; L2 is an intermediate layer featuring data that has undergone some form of transformation or cleaning; and L3 contains highly processed, optimized, and typically ready for analytics and decision-making processes.

Raw Data 69

Raw Data Data Business Intelligence Data Engineer 69

Striim

SEPTEMBER 11, 2024

Third-Party Data: External data sources that your company does not collect directly but integrates to enhance insights or support decision-making. These data sources serve as the starting point for the pipeline, providing the raw data that will be ingested, processed, and analyzed.

Data Pipeline 98

Data Pipeline Designing Data Lake Data Warehouse 98

Databand.ai

JULY 19, 2023

By loading the data before transforming it, ELT takes full advantage of the computational power of these systems. This approach allows for faster data processing and more flexible data management compared to traditional methods. The extraction process requires careful planning to ensure data integrity.

Data Cleanse 52

Data Cleanse Raw Data Data Storage Data Warehouse 52

Ascend.io

AUGUST 29, 2023

A star-studded baseball team is analogous to an optimized “end-to-end data pipeline” — both require strategy, precision, and skill to achieve success. Just as every play and position in baseball is key to a win, each component of a data pipeline is integral to effective data management.

Data Pipeline 52

Data Pipeline Pipeline-centric Database-centric Data Ingestion 52

ProjectPro

FEBRUARY 8, 2023

Do ETL and data integration activities seem complex to you? Read this blog to understand everything about AWS Glue that makes it one of the most popular data integration solutions in the industry. Did you know the global big data market will likely reach $268.4 Businesses are leveraging big data now more than ever.

AWS 98

AWS Scala Metadata Data Lake 98

AltexSoft

JUNE 26, 2023

Read our article on Hotel Data Management to have a full picture of what information can be collected to boost revenue and customer satisfaction in hospitality. While all three are about data acquisition, they have distinct differences. Data integration , on the other hand, happens later in the data management flow.

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 52

Precisely

JANUARY 11, 2024

Automation is a key driver in achieving digital transformation outcomes like agility, speed, and data integrity. These efforts include adopting automation platforms with flexible, contingent workflow solutions that drive efficiencies and greater data integrity across multiple complex, data-intensive processes.

Technology 59

Technology Data Validation Raw Data Portfolio 59

Cloudera

NOVEMBER 19, 2020

For many agencies, 80 percent of the work in support of anomaly detection and fraud prevention goes into routine tasks around data management. Inordinate time and effort are devoted to cleaning and preparing data, resulting in data bottlenecks that impede effective use of anomaly detection tools.

Government 118

Government Machine Learning Algorithm Raw Data 118

Cloudera

AUGUST 21, 2021

The same study also stated that having stronger online data security, being able to conduct more banking transactions online and having more real-time problem resolution were the top priorities of consumers. . Financial institutions need a data management platform that can keep pace with their digital transformation efforts.

Banking 101

Banking Raw Data High Quality Data Business Intelligence 101

Ascend.io

SEPTEMBER 19, 2023

In this article, we’ll delve into what is an automated ETL pipeline, explore its advantages over traditional ETL, and discuss the inherent benefits and characteristics that make it indispensable in the data engineering toolkit. ETL stands for: Extract: Retrieve raw data from various sources. The result?

ETL Tools 52

ETL Tools ETL Method Data Engineer Data Engineering 52

Databand.ai

AUGUST 30, 2023

Data testing tools: Key capabilities you should know Helen Soloveichik August 30, 2023 Data testing tools are software applications designed to assist data engineers and other professionals in validating, analyzing and maintaining data quality. There are several types of data testing tools.

Data Cleanse 52

Data Cleanse Data Pipeline Datasets Data Validation 52

ProjectPro

JANUARY 27, 2023

You have probably heard the saying, "data is the new oil". It is extremely important for businesses to process data correctly since the volume and complexity of raw data are rapidly growing. Data Warehousing - ETL tools and processes can be leveraged to load data into a data warehouse for reporting and analysis.

BI 52

BI ETL Tools Retail Healthcare 52

Monte Carlo

AUGUST 25, 2023

All these scalability and performance factors will inform your choice of data warehouse, lake, or lakehouse. Structure and schema Traditionally, data lakes excel at storing vast amounts of raw data — be it structured, semi-structured, or unstructured, without any specific constraints.

Data Lake 52

Data Lake Data Warehouse Unstructured Data Raw Data 52

Edureka

JULY 5, 2024

Tableau Prep has brought in a new perspective where novice IT users and power users who are not backward faithfully can use drag and drop interfaces, visual data preparation workflows, etc., simultaneously making raw data efficient to form insights. Validate data integrity at key stages to maintain accuracy throughout your flow.

Data Preparation 40

Data Preparation Process BI ETL Tools 40

The Modern Data Company

APRIL 15, 2024

Make your data work for you by integrating it seamlessly across the organization, increasing its accessibility, and therefore, its value. The Essence of Data Products Data Products aren’t merely tools or datasets; they are catalysts for transformation designed to bridge the gap between raw data and actionable insights.

Raw Data 52

Raw Data Government Data Datasets 52

ProjectPro

DECEMBER 7, 2021

Keeping data in data warehouses or data lakes helps companies centralize the data for several data-driven initiatives. While data warehouses contain transformed data, data lakes contain unfiltered and unorganized raw data. Monitoring: It is a component that ensures data integrity.

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 52

Monte Carlo

NOVEMBER 14, 2023

Here are a few disadvantages to consider before you take the plunge: Higher complexity: The maintenance required for Apache Iceberg tables is a significant drawback, requiring more data engineering resources spent on infrastructure maintenance rather than unlocking data value.

Data Lake 64

Data Lake Metadata Data Warehouse SQL 64